{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from sklearn.utils import class_weight\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_gaussian_noise(signal):\n",
    "    noise=np.random.normal(0,0.05,186)\n",
    "    return (signal+noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv('/Users/aring/jupyter/ECG/data/heartbeat/mitbih_train.csv',header=None)\n",
    "test_df=pd.read_csv('/Users/aring/jupyter/ECG/data/heartbeat/mitbih_test.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "        0         1         2         3         4         5         6    \\\n",
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       "\n",
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       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87554, 188)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[187]=train_df[187].astype(int)\n",
    "equilibre=train_df[187].value_counts()\n",
    "print(equilibre)\n",
    "plt.figure(figsize=(20,10))\n",
    "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
    "plt.pie(equilibre, labels=['n','q','v','s','f'], colors=['red','green','blue','skyblue','orange'],autopct='%1.1f%%')\n",
    "p=plt.gcf()\n",
    "p.gca().add_artist(my_circle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "df_1=train_df[train_df[187]==1]\n",
    "df_2=train_df[train_df[187]==2]\n",
    "df_3=train_df[train_df[187]==3]\n",
    "df_4=train_df[train_df[187]==4]\n",
    "df_0=(train_df[train_df[187]==0]).sample(n=20000,random_state=42)\n",
    "\n",
    "df_1_upsample=resample(df_1,replace=True,n_samples=20000,random_state=123)\n",
    "df_2_upsample=resample(df_2,replace=True,n_samples=20000,random_state=124)\n",
    "df_3_upsample=resample(df_3,replace=True,n_samples=20000,random_state=125)\n",
    "df_4_upsample=resample(df_4,replace=True,n_samples=20000,random_state=126)\n",
    "\n",
    "train_df=pd.concat([df_0,df_1_upsample,df_2_upsample,df_3_upsample,df_4_upsample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    20000\n",
      "3    20000\n",
      "2    20000\n",
      "1    20000\n",
      "0    20000\n",
      "Name: 187, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "equilibre=train_df[187].value_counts()\n",
    "print(equilibre)\n",
    "plt.figure(figsize=(20,10))\n",
    "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
    "plt.pie(equilibre, labels=['n','q','v','s','f'], colors=['red','green','blue','skyblue','orange'],autopct='%1.1f%%')\n",
    "p=plt.gcf()\n",
    "p.gca().add_artist(my_circle)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=train_df.groupby(187,group_keys=False).apply(lambda train_df : train_df.sample(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13d899550>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#normal heart beat \n",
    "plt.plot(c.iloc[0,:186])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(class_number,size,min_):\n",
    "    img=train_df.loc[train_df[187]==class_number].values\n",
    "    img=img[:,min_:size]\n",
    "    img_flatten=img.flatten()\n",
    "\n",
    "    final1=np.arange(min_,size)\n",
    "    for i in range (img.shape[0]-1):\n",
    "        tempo1=np.arange(min_,size)\n",
    "        final1=np.concatenate((final1, tempo1), axis=None)\n",
    "    print(len(final1))\n",
    "    print(len(img_flatten))\n",
    "    plt.hist2d(final1,img_flatten, bins=(80,80),cmap=plt.cm.jet)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a representation for all the class. We take all the signal and map them. Like that we have an estimation what the signal can look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1300000\n",
      "1300000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(0,70,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900000\n",
      "900000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(1,50,5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13cac4240>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plt.plot(c.iloc[1,:186])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600000\n",
      "600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(2,60,30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13cd68be0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plt.plot(c.iloc[2,:186])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700000\n",
      "700000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(3,60,25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13cf9f278>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(c.iloc[3,:186])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I use a fonction ( will depend of the version) where i add a noise to the data to generilize my train.\n",
    "\n",
    "tempo=c.iloc[0,:186]\n",
    "bruiter=add_gaussian_noise(tempo)\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(c.iloc[0,:186])\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(bruiter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train_df[187]\n",
    "target_test=test_df[187]\n",
    "y_train=to_categorical(target_train)\n",
    "y_test=to_categorical(target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train_df.iloc[:,:186].values\n",
    "X_test=test_df.iloc[:,:186].values\n",
    "\n",
    "X_train = X_train.reshape(len(X_train), X_train.shape[1],1)\n",
    "X_test = X_test.reshape(len(X_test), X_test.shape[1],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.1627907 ],\n",
       "        [0.54069769],\n",
       "        [0.75581396],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.99006623],\n",
       "        [0.93874174],\n",
       "        [0.34437087],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.97423887],\n",
       "        [0.93208432],\n",
       "        [0.59016395],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.79393941],\n",
       "        [0.69090909],\n",
       "        [0.57727271],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.97739506],\n",
       "        [0.93864369],\n",
       "        [0.88912809],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.87138265],\n",
       "        [0.6141479 ],\n",
       "        [0.55305469],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def network(X_train,y_train,X_test,y_test):\n",
    "    im_shape=(X_train.shape[1],1)\n",
    "    inputs_cnn=Input(shape=(im_shape), name='inputs_cnn')\n",
    "    conv1_1=Convolution1D(64, (6), activation='relu', input_shape=im_shape)(inputs_cnn)\n",
    "    conv1_1=BatchNormalization()(conv1_1)\n",
    "    pool1=MaxPool1D(pool_size=(3), strides=(2), padding=\"same\")(conv1_1)\n",
    "    conv2_1=Convolution1D(64, (3), activation='relu', input_shape=im_shape)(pool1)\n",
    "    conv2_1=BatchNormalization()(conv2_1)\n",
    "    pool2=MaxPool1D(pool_size=(2), strides=(2), padding=\"same\")(conv2_1)\n",
    "    conv3_1=Convolution1D(64, (3), activation='relu', input_shape=im_shape)(pool2)\n",
    "    conv3_1=BatchNormalization()(conv3_1)\n",
    "    pool3=MaxPool1D(pool_size=(2), strides=(2), padding=\"same\")(conv3_1)\n",
    "    flatten=Flatten()(pool3)\n",
    "    dense_end1 = Dense(64, activation='relu')(flatten)\n",
    "    dense_end2 = Dense(32, activation='relu')(dense_end1)\n",
    "    main_output = Dense(5, activation='softmax', name='main_output')(dense_end2)\n",
    "    \n",
    "    \n",
    "    model = Model(inputs= inputs_cnn, outputs=main_output)\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy',metrics = ['accuracy'])\n",
    "    \n",
    "    \n",
    "    callbacks = [EarlyStopping(monitor='val_loss', patience=8),\n",
    "             ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]\n",
    "\n",
    "    history=model.fit(X_train, y_train,epochs=40,callbacks=callbacks, batch_size=32,validation_data=(X_test,y_test))\n",
    "    model.load_weights('best_model.h5')\n",
    "    return(model,history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(history,X_test,y_test,model):\n",
    "    scores = model.evaluate((X_test),y_test, verbose=0)\n",
    "    print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
    "    \n",
    "    print(history)\n",
    "    fig1, ax_acc = plt.subplots()\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Model - Accuracy')\n",
    "    plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "    plt.show()\n",
    "    \n",
    "    fig2, ax_loss = plt.subplots()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Model- Loss')\n",
    "    plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.show()\n",
    "    target_names=['0','1','2','3','4']\n",
    "    \n",
    "    y_true=[]\n",
    "    for element in y_test:\n",
    "        y_true.append(np.argmax(element))\n",
    "    prediction_proba=model.predict(X_test)\n",
    "    prediction=np.argmax(prediction_proba,axis=1)\n",
    "    cnf_matrix = confusion_matrix(y_true, prediction)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 100000 samples, validate on 21892 samples\n",
      "Epoch 1/40\n",
      "100000/100000 [==============================] - 78s 780us/step - loss: 0.1900 - acc: 0.9319 - val_loss: 0.1788 - val_acc: 0.9374\n",
      "Epoch 2/40\n",
      "100000/100000 [==============================] - 83s 826us/step - loss: 0.0728 - acc: 0.9751 - val_loss: 0.1179 - val_acc: 0.9637\n",
      "Epoch 3/40\n",
      "100000/100000 [==============================] - 80s 798us/step - loss: 0.0485 - acc: 0.9839 - val_loss: 0.1216 - val_acc: 0.9629\n",
      "Epoch 4/40\n",
      "100000/100000 [==============================] - 70s 700us/step - loss: 0.0375 - acc: 0.9877 - val_loss: 0.1270 - val_acc: 0.9640\n",
      "Epoch 5/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0305 - acc: 0.9902 - val_loss: 0.1211 - val_acc: 0.9682\n",
      "Epoch 6/40\n",
      "100000/100000 [==============================] - 70s 698us/step - loss: 0.0249 - acc: 0.9920 - val_loss: 0.1897 - val_acc: 0.9505\n",
      "Epoch 7/40\n",
      "100000/100000 [==============================] - 73s 730us/step - loss: 0.0212 - acc: 0.9933 - val_loss: 0.1102 - val_acc: 0.9743\n",
      "Epoch 8/40\n",
      "100000/100000 [==============================] - 75s 748us/step - loss: 0.0179 - acc: 0.9945 - val_loss: 0.1245 - val_acc: 0.9722\n",
      "Epoch 9/40\n",
      "100000/100000 [==============================] - 70s 699us/step - loss: 0.0171 - acc: 0.9947 - val_loss: 0.1111 - val_acc: 0.9780\n",
      "Epoch 10/40\n",
      "100000/100000 [==============================] - 75s 748us/step - loss: 0.0148 - acc: 0.9956 - val_loss: 0.1262 - val_acc: 0.9736\n",
      "Epoch 11/40\n",
      "100000/100000 [==============================] - 69s 686us/step - loss: 0.0137 - acc: 0.9959 - val_loss: 0.1088 - val_acc: 0.9797\n",
      "Epoch 12/40\n",
      "100000/100000 [==============================] - 69s 688us/step - loss: 0.0121 - acc: 0.9962 - val_loss: 0.1279 - val_acc: 0.9760\n",
      "Epoch 13/40\n",
      "100000/100000 [==============================] - 69s 694us/step - loss: 0.0114 - acc: 0.9965 - val_loss: 0.1207 - val_acc: 0.9774\n",
      "Epoch 14/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0109 - acc: 0.9968 - val_loss: 0.1412 - val_acc: 0.9709\n",
      "Epoch 15/40\n",
      "100000/100000 [==============================] - 69s 689us/step - loss: 0.0108 - acc: 0.9968 - val_loss: 0.1303 - val_acc: 0.9769\n",
      "Epoch 16/40\n",
      "100000/100000 [==============================] - 69s 690us/step - loss: 0.0079 - acc: 0.9974 - val_loss: 0.1313 - val_acc: 0.9783\n",
      "Epoch 17/40\n",
      "100000/100000 [==============================] - 70s 702us/step - loss: 0.0100 - acc: 0.9972 - val_loss: 0.1265 - val_acc: 0.9788\n",
      "Epoch 18/40\n",
      "100000/100000 [==============================] - 69s 686us/step - loss: 0.0072 - acc: 0.9977 - val_loss: 0.1644 - val_acc: 0.9726\n",
      "Epoch 19/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0086 - acc: 0.9976 - val_loss: 0.1271 - val_acc: 0.9800\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, Convolution1D, MaxPool1D, Flatten, Dropout\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import keras\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "model,history=network(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.97%\n",
      "<keras.callbacks.History object at 0x13d1ea048>\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'accuracy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-e4dd497b4d79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevaluate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-c7f81c0b7d46>\u001b[0m in \u001b[0;36mevaluate_model\u001b[0;34m(history, X_test, y_test, model)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mfig1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'accuracy'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(history,X_test,y_test,model)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-8070a62e65b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;31m# Compute confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mcnf_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_printoptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(10, 10))\n",
    "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],normalize=True,\n",
    "                      title='Confusion matrix, with normalization')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sources\n",
    "https://www.kaggle.com/gregoiredc/arrhythmia-on-ecg-classification-using-cnn/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
